Description

This is the dataset repository used in the pyiqa toolbox. Please refer to Awesome Image Quality Assessment for details of each dataset Example commandline script with huggingface-cli: huggingface-cli download chaofengc/IQA-PyTorch-Datasets live.tgz --local-dir ./datasets --repo-type dataset cd datasets tar -xzvf live.tgz

  Disclaimer for This Dataset Collection

This collection of datasets is compiled and maintained for academic, research, and educational… See the full description on the dataset page: https://huggingface.co/datasets/chaofengc/IQA-PyTorch-Datasets.

Context

On the Pytorch website, you'll find a bunch of tutorials. This Dataset is the one used for the dataloading one.

Content

It contains some faces images and a CSV file with their respective landmarks points.

Acknowledgements

Thanks for pytorch to provide comprehensive material to get a grasp about their framework. https://pytorch.org/tutorials/beginner/data_loading_tutorial.html

Inspiration

Play with pytorch or check how much simpler it is to accomplish this kind of task with a different framework.

https://github.com/pytorch/pytorch/raw/main/docs/source/_static/img/pytorch-logo-dark.png" alt="PyTorch Logo">

PyTorch is a Python package that provides two high-level features: - Tensor computation (like NumPy) with strong GPU acceleration - Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Our trunk health (Continuous Integration signals) can be found at hud.pytorch.org.

• More About PyTorch
  • A GPU-Ready Tensor Library
  • Dynamic Neural Networks: Tape-Based Autograd
  • Python First
  • Imperative Experiences
  • Fast and Lean
  • Extensions Without Pain
• Installation
  • Binaries
  • NVIDIA Jetson Platforms
  • From Source
  • Prerequisites
    • NVIDIA CUDA Support
    • AMD ROCm Support
    • Intel GPU Support
  • Get the PyTorch Source
  • Install Dependencies
  • Install PyTorch
    • Adjust Build Options (Optional)
  • Docker Image
  • Using pre-built images
  • Building the image yourself
  • Building the Documentation
  • Previous Versions
• Getting Started
• Resources
• Communication
• Releases and Contributing
• The Team
• License

More About PyTorch

Learn the basics of PyTorch

At a granular level, PyTorch is a library that consists of the following components:

Usually, PyTorch is used either as:

• A replacement for NumPy to use the power of GPUs.
• A deep learning research platform that provides maximum flexibility and speed.

Elaborating Further:

A GPU-Ready Tensor Library

If you use NumPy, then you have used Tensors (a.k.a. ndarray).

PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the computation by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, reductions. And they are fast!

Dynamic Neural Networks: Tape-Based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world. One has to build a neural network and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as torch-autograd, autograd, Chainer, etc.

While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy resear...

Pytorch

## Overview

Pytorch is a dataset for object detection tasks - it contains Test annotations for 1,277 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Convert Voc To Pytorch

## Overview

Convert Voc To Pytorch is a dataset for object detection tasks - it contains Ls annotations for 11,828 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Description

This repo contains the meta information of datasets stored in chaofengc/IQA-PyTorch-Weights. They are used in the training codes of the pyiqa toolbox.

  Disclaimer for Datasets Included

This collection of datasets is compiled and maintained for academic, research, and educational purposes. It is important to note the following points regarding the datasets included in this Collection:

Rights & Permissions: Each dataset in this Collection is the property of its… See the full description on the dataset page: https://huggingface.co/datasets/chaofengc/IQA-PyTorch-Datasets-metainfo.

Fish PYTORCH

## Overview

Fish PYTORCH is a dataset for object detection tasks - it contains Fishes annotations for 2,975 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Traffic analytics, rankings, and competitive metrics for pytorch.org as of October 2025

Context

This is a dataset for the TecoGan Pytorch model. The Github repo can be found here.

Content

There are 400 scenes from the UCF101 dataset. Each video was split into photos with a maximum length of 120 photos. The photos were put into this dataset in the format that the TecoGan dataloader takes.

Acknowledgements

The original UCF101 dataset can be found here. And you can find the original TecoGan repo here.

Inspiration

Let's see how good your super resolution images can look. How close can you get to the original?

Test Yolo V5 PyTorch

## Overview

Test Yolo V5 PyTorch is a dataset for object detection tasks - it contains Test annotations for 637 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Alexander444

Contents

Pytorch tutorials and auxiliary files

Dataset Card for github-pytorch-issues

  Dataset Summary

This dataset is a curated collection of GitHub issues from the PyTorch repository. Each entry includes the issue title, body, user, state, labels, comments, and other relevant fields that are useful for tasks such as text classification, semantic search, and question answering.

  Supported Tasks and Leaderboards

The dataset supports the following tasks:

Open-domain Question Answering: Given a user query… See the full description on the dataset page: https://huggingface.co/datasets/mayankpuvvala/github-pytorch-issues.

kye/all-pytorch-code dataset hosted on Hugging Face and contributed by the HF Datasets community

Samples in this benchmark were generated by RELAI using the following data source(s): Data Source Name: pytorch Data Source Link: https://pytorch.org/docs/stable/index.html Data Source License: https://github.com/pytorch/pytorch/blob/main/LICENSE Data Source Authors: PyTorch AI Benchmarks by Data Agents. 2025 RELAI.AI. Licensed under CC BY 4.0. Source: https://relai.ai

Yolo To COCO For SSD Pytorch

## Overview

Yolo To COCO For SSD Pytorch is a dataset for object detection tasks - it contains Car Van Truck Bus Person Cyclist annotations for 5,981 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

The SloNER is a model for Slovenian Named Entity Recognition. It is is a PyTorch neural network model, intended for usage with the HuggingFace transformers library (https://github.com/huggingface/transformers).

The model is based on the Slovenian RoBERTa contextual embeddings model SloBERTa 2.0 (http://hdl.handle.net/11356/1397). The model was trained on the SUK 1.0 training corpus (http://hdl.handle.net/11356/1747).The source code of the model is available on GitHub repository https://github.com/clarinsi/SloNER.

Subset of the SMDG-19 for Glaucoma dataset in PyTorch Format

SMDG-19: https://www.kaggle.com/datasets/deathtrooper/multichannel-glaucoma-benchmark-dataset

Contains Train, Val and Test set of Fundus images for Glaucoma Detection

2 Classes (0|1)

1: Glaucoma Present 0: Glaucoma not Present

Pothole Yolov3 Pytorch

## Overview

Pothole Yolov3 Pytorch is a dataset for object detection tasks - it contains Pothole annotations for 665 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for "pytorch-tutorial-168"

More Information needed

Dataset Card for "pytorch-discuss-tutorial-346"

More Information needed